RF carrier synchronization and phase alignment methods and systems

ABSTRACT

A method comprising generating a baseband information signal by mixing a received modulated carrier signal with a local oscillator (LO) signal having an LO frequency; obtaining baseband signal samples of the baseband information signal having a baseband signal magnitude and a baseband signal phase; determining a cumulative phase measurement associated with baseband signal samples having a baseband signal magnitude greater than a threshold; and, applying a correction signal to compensate for an LO frequency offset of the LO frequency based on the cumulative phase.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a non-provisional of, and claims benefitunder 35 U.S.C. §119(e) from U.S. Provisional Patent Application Ser.No. 61/708,116 entitled “RF Carrier Synchronization and Phase AlignmentMethods and Systems”, the entire contents of which being incorporatedherein by reference.

BACKGROUND OF THE INVENTION

Conventional synchronization techniques used between base stations relyon GPS signals, which broadcast precision time stamps as well as a 1 Hzreference signal. Base stations can extract this timing informationthrough a demodulation process and use them to acquire accurate time aswell as the frequency reference needed for training local oscillators,such as VCXOs or OCXOs, whose accuracy, although quite accurate, byitself is not accurate enough for use in base stations. With theexpected proliferation of smaller-sized base stations—henceforth werefer them collectively as ‘microcell’ base stations—GPS-based solutionscould be either (1) too expensive an option if the microcell basestation were to contain a separate GPS receiver, or (2) unavailable dueto the environment in which the microcell base station is located. Theonly other alternative carrier synchronization methodology accurateenough to meet the needs of 4G and beyond is Precision Time Protocol(PTP) defined in the IEEE 1588 standard. PTP based on IEEE 1588 relieson availability of Ethernet through a wireline access, which may notalways be available for a given microcell environment. For instance, astandalone microcell that also provides a back-haul communication linkto the macro base stations could be located somewhere without a wiredEthernet access and in need of a synchronization method.

Accordingly, there is a need for improved RF synchronization and phasealignment systems and methods.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a plot of a complex IQ baseband signal in accordance with someembodiments.

FIG. 2 is a flowchart of a plot of cumulative phase values in accordancewith some embodiments.

FIG. 3 is a schematic of a frequency locking system in accordance withsome embodiments.

FIG. 4 is a schematic of a frequency locking system in accordance withsome embodiments.

FIG. 5 is a flowchart of a frequency locking algorithm in accordancewith some embodiments.

FIG. 6 is a plot of a frequency error based on the algorithm of afrequency lock algorithm in accordance with some embodiments.

FIG. 7 is a plot of an IQ correlation calculation in accordance withsome embodiments.

FIGS. 8-10 are plots of an IQ correlation calculation in accordance withsome embodiments.

FIGS. 11-12 are flowcharts of frequency locking algorithms in accordancewith some embodiments.

FIG. 13 is a timing diagram of a time synchronization algorithm inaccordance with some embodiments.

FIG. 14 is a diagram of a positioning algorithm in accordance with someembodiments.

FIG. 15 is a diagram of a angle of arrival positioning algorithm inaccordance with some embodiments.

FIG. 16 is a diagram of a positioning algorithm in accordance with someembodiments.

FIGS. 17 and 18 are message flow diagrams of positioning algorithms inaccordance with some embodiments.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment, a method comprises: generating a baseband informationsignal by mixing a received modulated carrier signal with a localoscillator (LO) signal having an LO frequency; obtaining baseband signalsamples of the baseband information signal having a baseband signalmagnitude and a baseband signal phase; determining a cumulative phasemeasurement associated with baseband signal samples having a basebandsignal magnitude greater than a threshold; and, applying a correctionsignal to compensate for an LO frequency offset of the LO frequencybased on the cumulative phase.

In a further embodiment, a method comprises: generating a basebandinformation signal by mixing a received modulated carrier signal with alocal oscillator (LO) signal having an LO frequency; obtaining basebandsignal samples of the baseband information signal having an in-phasesignal sample and a quadrature signal sample; determining an offsetfrequency rotation based on an estimated correlation between thein-phase signal samples and the quadrature signal samples; and,processing the baseband information signal using the offset frequencyrotation.

Described herein are the above embodiments as well as additionalembodiments, some of which are particularly useful to perform radiofrequency (RF) carrier synchronization for use in wirelesscommunications. Advanced wireless communication networks such as 4G LTE,and LTE-advanced, require a minimum accuracy of 0.05 parts per million(50 ppb) for the carrier frequency. Conventional approaches to thisrequirement have been to incorporate GPS receivers in the system inorder to synchronize time and frequency between base stations. However,with increasing needs for micro-cell, and pico-cell base stations,relying on GPS-based solution becomes either too expensive orunavailable as an option. Presented in this document are two alternativeways to achieving accurate RF carrier synchronization as well as phasealignment at the physical-layer level. The synchronization methodsdescribed here can be implemented in any modulated communication systemindependent of the modulation method. The technology described here canmeet the 2nd source needs for the carrier synchronization critical forthe economical high-volume deployment of small-cell base stations.

The methods described herein achieve carrier synchronization betweennodes of a communication network without any extra source outside of thecommunication network, such as the GPS signal or Ethernet connectivity.Instead, it utilizes the characteristics of the received RF signal andaccomplishes highly accurate synchronization, which in some embodimentsmay be used to train the crystal oscillators (XOs) in the receiver endand maintaining accurate timing information. This new method allows themicrocell to become the primary source for clock and timing referencefor the subsequent wired or wireless networks that it connects to.

Embodiments of the carrier synchronization methods presented hereinclude at least (i) coarse and wide-range synchronization methods and(ii) precise narrow-range synchronization methods. The coarse andwide-range synchronization methods achieves the accuracy of about 0.1parts per million (or 100 ppb) without any fundamental range limit. Theprecise narrow-range synchronization technique achieves frequency lockbetter than 1 part per billion (<1 ppb) and at the same timeaccomplishes phase alignment for the demodulated baseband signal.

Embodiments of the wide range synchronization techniques make use of therandom nature of the quadrature modulated signal. Quadrature modulatedsignals in general have random instantaneous amplitude and phase.However, if we try to use the random nature of its instantaneous phaseof a modulated signal in BB as a reference for synchronization, we findthat its cumulative long-run average does not necessarily converge to acumulative zero-rotation. Furthermore, the busier the basebandconstellation is, the more random its cumulative phase rotation tends toget. This phenomenon is due in part to the fact that when the magnitudeof an instantaneous sample is smaller it can produce more drastic phasechanges within a given sample interval than when the sample amplitude islarger. An extreme example would be when the signal crosses the originon I-Q plan within one sample period thus producing 180 degrees of phaseshift. On the other hand, when the signal is near its maximum amplitudethe incremental angle changes it can produce between samples is thesmallest since it is furthest away from the origin.

The synchronization technique described here utilizes this fact thatwhen the amplitude is large its phase rotation tends to be limited, andconsequently, it makes it easier to observe the excess phase rotationcaused by the mismatch between the transmitted frequency and thedemodulation frequency in receiver.

In order to illustrate this phenomenon several simulation results areshown. With respect to FIG. 1, a plot of 64-QAM baseband signals withrandomly generated I and Q data is shown. The plot tracks the signalphase and magnitude of the baseband signal phase after downconversionfrom the carrier frequency. With respect to FIG. 2, the cumulative phaserotation over time 202 is shown, and as can be seen, the overallcumulative rotation, when all samples are accounted for, doesn't seem toconverge to zero over time. Instead, it tends to wander away as timepasses. However, when only higher magnitude signals are considered (80%in this example), the cumulative phase stays quite close to zerorotation in the absence of a frequency offset error. Thus, when onlysamples above certain threshold (80% or higher in this example) areconsidered, the wandering effect seems to get reduced drastically asshown in plot 204 in FIG. 2. For a fair comparison a longer period istaken for the 80% simulation in order to capture the same number ofsamples used to calculate cumulative angular rotation. The simulationshows a drastically reduced cumulative phase when we consider onlylarger amplitudes despite the fact that both cases come from the samenumber of samples. Simulation result above with 80% threshold shows nomore than π/2 radian of cumulative angular rotation over 10 milliontotal samples. Other thresholds may be used in various embodiments.

FIG. 3 shows one example of how the overall system can be implementedthat achieves carrier synchronization using the method described above.The simplified block diagram of a sample open-loop system is shown inFIG. 3. In this embodiment, after each iteration, the offset value willbe updated based on the averaged δφ(i). The speed by which this offsetvalue is updated numerically, effectively decides how quickly itconverges to the final value. In various embodiments, the update speedmay be altered in software as the iteration progresses. For example, inthe beginning the system may update faster and gradually slow down theupdate rate as the iteration progresses and offset value convergestoward the final value. This variable update speed shortens overallconvergence time. One update method can be a form of accumulator withhigher order transfer function, which is equivalent to having a higherorder loop filter in a closed loop system.

A closed loop system is depicted in FIG. 4. The closed loop systemadjusts VCXO as shown by signal path 402 in order to synchronize withreceived RF Carrier using the method described herein. The thresholdfactor, a, may be used to determine how much of the larger amplitudesignals will be considered. In simulations shown here, threshold α=0.8is used, which means only the samples with 80% of peak or larger areconsidered for phase comparison.

With respect to FIG. 5, a method 500 according to one embodiment isdescribed herein. The method comprises: generating a basebandinformation signal at 502 by mixing a received modulated carrier signalwith a local oscillator (LO) signal having an LO frequency; obtainingbaseband signal samples of the baseband information signal at 504 havinga baseband signal magnitude and a baseband signal phase; determining acumulative phase measurement associated with baseband signal samples at506 having a baseband signal magnitude greater than a threshold; and,applying a correction signal at 508 to compensate for an LO frequencyoffset of the LO frequency based on the cumulative phase.

The LO signal generally includes an in-phase carrier signal and aquadrature carrier signal, and the baseband signal samples aredetermined from an in-phase channel sample and a quadrature channelsample. Such LO structures are well-known, and may include a crystaloscillator and a signal splitter, wherein one of the signal branchesincludes a 90 degree phase offset to generate the quadrature carriercomponent. Determining the cumulative phase measurement may compriseaccumulating a plurality of differential phases, wherein each of theplurality of differential phases is a phase difference between aninitial signal point and end signal point, each of the initial signalpoint and end signal point having magnitudes greater than a threshold a.In an alternative embodiment, the phase differentials between successiveIQ points may each be calculated for IQ points where the magnitudes areabove the threshold. In some embodiments, the cumulative phasemeasurement is determined over either (i) a predetermined time intervalor (ii) a predetermined number of samples. The time interval or thenumber of samples may be used to determine an average phaseoffset/interval or phase offset/time.

The method of applying a correction signal comprises in one embodimentadjusting an LO control signal. The LO control signal may be a tuningvoltage based on a low-pass filtered version of the cumulative phase. Inan alternative embodiment, the method of applying a correction signalcomprises applying a complex rotation to the baseband signal samples.The method of some embodiments may further comprise re-determining thecumulative phase measurement after applying the correction signal.Further embodiments may include iteratively determining the cumulativephase measurement and responsively adjusting the correction signal. Thecorrection signal may also be updated by adjusting a loop filtercharacteristic, such that an initial large offset may be quicklyadjusted for with a large update coefficient, and over time the updatecoefficient may be reduced to provide a lower loop bandwidth andconvergence with less overshoot.

In further embodiments, an apparatus such as shown in FIG. 3 maycomprise: a demodulator having a mixer 306 and a local oscillator (LO)308 configured to generate a baseband information signal by mixing areceived modulated carrier signal with an LO signal having an LOfrequency; an analog to digital converter 316 configured to generatebaseband signal samples of the baseband information signal; a phaseaccumulator 318 configured to receive the baseband signal samples and todetermine a cumulative phase measurement associated with baseband signalsamples having a baseband signal magnitude greater than a threshold;and, an LO correction module 318 configured to apply a correction signalto compensate for an LO frequency offset of the LO frequency based onthe cumulative phase. The LO 308 includes an in-phase carrier signalgenerator and a quadrature carrier signal generator. The phaseaccumulator 318 comprises a magnitude and phase converter configured togenerate magnitude and phase information based on an in-phase basebandsignal sample and a quadrature baseband signal sample, a differentialphase module configured to determine differential phase values, and amagnitude threshold comparator configured to identify differential phasevalues corresponding to magnitudes greater than a threshold. The LOcorrection module may comprise an LO control module configured togenerate a control signal 402. The LO correction module may comprise alow-pass filter configured to generate the control signal 402 in theform of a tuning voltage based on filtered version of the cumulativephase. The LO correction module may be configured to adjust a loopfilter characteristic.

In alternative embodiments, the LO correction module comprises a complexrotation module configured to rotate the baseband signal samples. Thistype of correction may be performed in open loop such that correctionsare not fed back to the LO itself, but the frequency rotation errorinduced by the LO error is corrected by the complex multiplication by acomplex sinusoid.

The phase accumulator in some embodiments is further configured tore-determine the cumulative phase measurement after the LO correctionmodule has applied the correction signal. The apparatus may beconfigured to iteratively determining the cumulative phase measurementand responsively adjusting the correction signal.

Thus, the instantaneous phase increment, δφ(i), between two adjacentsamples in the baseband may be averaged over N number of samples, andthe average is fed back to the loop filter, which generates tuningvoltage for VCXO. This creates a feedback system where the number of theaveraged samples (N) and loop filter characteristics affects the overallfeedback loop transfer function, which in turn determines the adaptationspeed of the VCXO. Simulation shows the slower the adaptation speed(i.e. smaller loop gain) the more accurately the VCXO converges to thecorrect value. But it comes at the expense of longer settling time.Simulations also show that larger values of threshold a provide bettersystem convergence. However, this comes at the cost of having to takemuch longer time for the control loop to settle. FIG. 6 depicts theclosed loop simulation results that show convergence behavior of thecarrier synchronization loop. It shows that with this method thesynchronization error can be reduced to about 100 parts per billion (0.1ppm).

In a further embodiment, higher precision narrow-range synchronizationtechniques are provided. The RF synchronization techniques describedherein makes use of the fundamentally uncorrelated nature of the twoquadrature signals, namely the in-phase (I) and quadrature-phase (Q)signals that are modulated into the carrier waveform. In addition, itutilizes the fact that when the correlation of I and Q signals aremonitored within a set of data taken over a window of time interval,small mismatches in the transmitted carrier frequency and demodulatingfrequency can be observed if the synchronization error is within certainrange. Therefore, this method can work well with the wide-rangesynchronization technique described in previous section, which can beapplied first in order to obtain a “coarse” synchronization. Thisnarrow-range synchronization technique can then be applied afterward inorder to improve further the synchronization accuracy. In alternativeembodiments, the narrow-range techniques may be used independently ofthe coarse synchronization such as if the tolerances of the transmit andreceive frequencies result in smaller frequency offsets.

When the quadrature signal is modulated with the carrier frequencyω_(c), the received RF signal can be expressed as follows:

$\begin{matrix}{{y(t)} = {{A(t)}{\cos\left( {{\omega_{c}t} + {\phi(t)}} \right)}}} & {{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}(3.1)} \\{= {{{I(t)}{\cos\left( {\omega_{c}t} \right)}} - {{Q(t)}{\sin\left( {\omega_{c}t} \right)}}}} & {(3.2)}\end{matrix}$ where ${A(t)} = \sqrt{{I^{2}(t)} + {Q^{2}(t)}}$${\phi(t)} = {\arctan\left( \frac{Q(t)}{I(t)} \right)}$

When the receiver has a frequency error of −ε, its demodulationfrequency, ω₀ can be expressed as:ω_(c)=ω₀+ε  (3.3)

And the received RF signal, r(t) can be expressed as follows:

$\begin{matrix}{\begin{matrix}{{r(t)} = {{{I(t)} \cdot {\cos\left( {\omega_{c}t} \right)}} - {{Q(t)} \cdot {\sin\left( {\omega_{c}t} \right)}}}} \\{= {{{I(t)} \cdot {\cos\left( {{\omega_{o}t} + {ɛ\; t}} \right)}} - {{Q(t)} \cdot {\sin\left( {{\omega_{o}t} + {ɛ\; t}} \right)}}}} \\{= {{{I(t)}\left\{ {{{\cos\left( {\omega_{o}t} \right)}{\cos\left( {ɛ\; t} \right)}} - {{\sin\left( {\omega_{o}t} \right)}{\sin\left( {ɛ\; t} \right)}}} \right\}} -}} \\{{Q(t)}\left\{ {{{\sin\left( {\omega_{o}t} \right)}{\cos\left( {ɛ\; t} \right)}} + {{\cos\left( {\omega_{o}t} \right)}{\sin\left( {ɛ\; t} \right)}}} \right\}} \\{= {{\left\{ {{{I(t)}{\cos\left( {ɛ\; t} \right)}} - {{Q(t)}{\sin\left( {ɛ\; t} \right)}}} \right\}{\cos\left( {\omega_{o}t} \right)}} -}} \\{\left\{ {{{Q(t)}{\cos\left( {ɛ\; t} \right)}} + {{I(t)}{\sin\left( {ɛ\; t} \right)}}} \right\}{\sin\left( {\omega_{o}t} \right)}} \\{= {{{I_{R}(t)} \cdot {\cos\left( {\omega_{c}t} \right)}} - {{Q_{R}(t)} \cdot {\sin\left( {\omega_{c}t} \right)}}}}\end{matrix}{where}} & (3.4) \\{{I_{R}(t)} = {{{I(t)}{\cos\left( {ɛ\; t} \right)}} - {{Q(t)}{\sin\left( {ɛ\; t} \right)}}}} & (3.4) \\{{Q_{R}(t)} = {{{Q(t)}{\cos\left( {ɛ\; t} \right)}} + {{I(t)}{\sin\left( {ɛ\; t} \right)}}}} & (3.4)\end{matrix}$

Here, I_(R)(t) and Q_(R)(t) represent the demodulated baseband signalscontaining a frequency synchronization error. The above equations showthat due to the synchronization error the demodulated quadrature signalwill show some correlation between I_(R)(t) and Q_(R)(t) stemming fromthe frequency error ε. In particular, we are interested in correlationbetween {I_(R)}² and {Q_(R)(t)}² whose cross-covariance is defined asbelow. First, we define the values A and B as the squares of thein-phase and quadrature signals:

$\begin{matrix}{A\overset{\Delta}{=}{{\left\{ {I_{R}(t)} \right\}^{2}\mspace{14mu}{and}\mspace{14mu} B}\overset{\Delta}{=}\left\{ {Q_{R}(t)} \right\}^{2}}} & (3.7)\end{matrix}$and their mean values to be μ_(A) and μ_(B) respectively. Then theircross-covariance can be expressed as follows:

$\begin{matrix}{{C\left( {A,B} \right)} = {E\left\{ {\left( {A - \mu_{A}} \right)\left( {B - \mu_{B}} \right)} \right\}}} & {{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}(3.8)} \\{= {{E\left\{ {AB} \right\}} - {\mu_{A}E\left\{ B \right\}} - {\mu_{B}E\left\{ A \right\}} + {\mu_{A}\mu_{B}}}} & {(3.9)} \\{= {{E\left\{ {AB} \right\}} - \mu^{2}}} & {(3.10)}\end{matrix}$if we assume:μ=μ_(A)=μ_(B)

Now if we expand the first term of the cross-covariance in equation(3.10), we get the following:

$\begin{matrix}\begin{matrix}{{E\left\{ {AB} \right\}} = {E\left\{ {\left( {I_{R}(t)} \right)^{2}\left( {Q_{R}(t)} \right)^{2}} \right\}}} \\{= {E\left\{ {\left( {{{I(t)}{\cos\left( {ɛ\; t} \right)}} - {{Q(t)}{\sin\left( {ɛ\; t} \right)}}} \right)^{2} \cdot \left( {{{Q(t)}{\cos\left( {ɛ\; t} \right)}} + {{I(t)}{\sin\left( {ɛ\; t} \right)}}} \right)^{2}} \right\}}} \\{= {E\left\{ \left\lbrack {{\left( {{I^{2}(t)} - {Q^{2}(t)}} \right){\sin\left( {ɛ\; t} \right)}{\cos\left( {ɛ\; t} \right)}} - {{I(t)}{Q(t)}\begin{pmatrix}{{\sin^{2}\left( {ɛ\; t} \right)} -} \\{\cos^{2}\left( {ɛ\; t} \right)}\end{pmatrix}}} \right\rbrack^{2} \right\}}}\end{matrix} & (3.11)\end{matrix}$

Here we can see the expectation of the first term goes to zero.Therefore, we get:

$\begin{matrix}\begin{matrix}{{E\left\{ {AB} \right\}} = {E\left\{ \left\lbrack {{I(t)}{Q(t)}\left( {{\sin^{2}\left( {ɛ\; t} \right)} - {\cos^{2}\left( {ɛ\; t} \right)}} \right)} \right\rbrack^{2} \right\}}} \\{= {E\left\{ \left\lbrack {{I(t)}{Q(t)}{\cos\left( {2\; ɛ\; t} \right)}} \right\rbrack^{2} \right\}}} \\{= {E\left\{ {\left( {{I_{R}(t)}{Q_{R}(t)}} \right)^{2}\left( \frac{1 + {\cos\left( {4\; ɛ\; t} \right)}}{2} \right)} \right\}}} \\{= {E\left\{ \left( {{I_{R}(t)}{Q_{R}(t)}} \right)^{2} \right\} E\left\{ \frac{1 + {\cos\left( {4\; ɛ\; t} \right)}}{2} \right\}}} \\{= {{\mu^{2} \cdot E}\left\{ \frac{1 + {\cos\left( {4\; ɛ\; t} \right)}}{2} \right\}}}\end{matrix} & (3.12)\end{matrix}$

As expected when there is no synchronization error (i.e., ε=0), thecross-covariance, referred to herein as one type of correlation value,goes to zero since E{AB}=μ². However, when ε≠0 equation (3.12) tells usthat the cross-covariance of I²(t) and Q²(t) oscillates due to cos(4εt)term. This mathematical derivation provides the basis for thehigh-precision synchronization technology described herein.

As a slight variant to above approach, in another embodiment we define Aand B in equation (3.7) as the absolute values of the in-phase andquadrature signals:

$\begin{matrix}{A\overset{\Delta}{=}{{{{I_{R}(t)}}\mspace{14mu}{and}\mspace{14mu} B}\overset{\Delta}{=}{{Q_{R}(t)}}}} & (3.13)\end{matrix}$

This will yield effectively the same desired results with the slightchange in the cross-covariance or correlation expression as follows:C(A,B)=E{AB}−μ ²  (3.14)E{AB}=μ ² ·E{cos(2εt)}  (3.15)

A High-Precision Synchronization Procedure will now be described. Notingthat the only non-constant term of the cross covariance expressed inequation (3.10) is the term E {(I_(R)(t))²·(Q_(R)(t))²} as re-writtenbelow:

$\begin{matrix}{{{E\left\{ {\left( {I_{R}(t)} \right)^{2} \cdot \left( {Q_{R}(t)} \right)^{2}} \right\}} = {{\mu^{2} \cdot E}\left\{ \frac{1 + {\cos\left( {4\; ɛ\; t} \right)}}{2} \right\}}},} & (3.16)\end{matrix}$some embodiments utilize a sufficiently long observation window, ΔT inorder to observe sufficient changes in cos(4εt) term. If 4εΔT=π/2 andε=200 Hz for example, then the following interval is obtained:

${\Delta\; T} = {\frac{\pi}{8\; ɛ} = {\frac{\pi}{8(200)} = {1.96\mspace{14mu}{msec}}}}$

If the de-modulated baseband signal is sampled at 100 MHz for 1.96 ms ofobservation window, this translates to 196,350 samples.

Using the alternative definition of A and B as in equation (3.13), thenon-constant term is:E{|I _(R)(t)|·|Q _(R)(t)|}=μ² ·E{|cos(2εt)|}  (3.17)

Then, for 2εΔT=π/2, 392,700 samples may be used. Although the lattercase requires twice the sample size, it does not require a squaringoperation of the samples as in former case, thus from here on, thelatter case is used to illustrate various further embodiments forsimplicity. In embodiments where a received RF signal is at 2 GHz andits down conversion at the receiver was done using a LO frequency sourcewith accuracy of 100 parts per billion, this translates to the frequencyerror, ε of +/−200 Hz. Once the sample is taken it is known that theinitial frequency error is within the initial error bound of +/−200 Hz.The frequency error (synchronization error) in the sampled data may becorrected by applying the amount of frequency error correction (as extraphase) to the sampled data within the expected error bound with finiteincrements while observing the cross covariance value.

More specifically, we will observe the following correlation value:

$\begin{matrix}{S = {\frac{1}{N}{\sum\limits_{k = 1}^{N}\left\{ {{{I_{R}(k)}} \cdot {{Q_{R}(k)}}} \right\}}}} & (3.18)\end{matrix}$Expressed in an alternative form, a set of values may be determinedfrom:

${S(i)} = {\sum\limits_{k = 1}^{N}\left\{ {{{I_{R,i}(k)}} \cdot {{Q_{R,i}(k)}}} \right\}}$

where N represents the total number of samples, and S(i) is calculatedby iterating over i various possible frequency error correctionsperformed on the original observation data. That is, the I_(R,i)(k) andQ_(R,i) (k) are rotated versions of the I and Q samples whose phase hasbeen corrected with an i'th frequency error correction. For instance, iffrequency correction amounts have 20 Hz increments from −200 Hz to +200Hz, there will be 21 sets of S(i) with i ranging from 1 to 21. If theactual frequency error was 122 Hz, then, the correction of −120 Hz willshow the best correction resulting in highest S(i) value in the set.Shown in FIGS. 7 and 8 are simulation results using 64-QAM modulatedsignal with and without frequency error. First, FIG. 7 shows 10different plots taken with 10 different set of samples with no frequencysynchronization error. Each plot has 21 points showing +/−10 incrementalfrequency adjustments made for the given set of samples. As expected noadjustment case (mid-point) consistently shows the best result for allten plots—highest value means least correlation between |IR| & |QR|. Themultiple plots (10 in total) are shown in the figures in order toillustrate that the results are consistent given any set of data despitethe fact that this method fundamentally relies on the statistical natureof the data. This is due to the fact that each plot is effectively anintegral (or summation) of the total samples, N as expressed in equation(3.18) resulting in the outcome free of the random nature of theindividual samples.

FIG. 8 shows repeated simulation with 10 new sets of samples with afrequency offset synchronization error. Then a total of +/−10incremental frequency adjustments were made for each set of samples. Allten results consistently indicate that the sixth adjustment from left(−12) accomplishes the best frequency offset and synchronization errorcorrection.

Using this information the system can correct the frequency error byfiguring out how much correction is needed from tabulation. With eachiteration the algorithm can determine the precise amount of frequencycorrection required by reducing the increment to a smaller value whileincreasing the number of samples. As the error gets smaller with eachiteration, the required sample size may get larger as the period ofcos(2εt) in equation (3.17) gets longer. Simulations show better thanone part per billion accuracy of synchronization can be achieved usingthis method even after accounting for noise level and I-Q mismatchesexpected in a real system.

If a desired outcome is just to maintain an accurate offset of theexisting clock source in the receiver, precise frequency error (or errorin reference crystal oscillator) can be extracted through this methodand the local receiver's system clock can be updated in digital domainusing the procedure described above (i.e., via a complex rotation of theIQ sample data). On the other hand, if the final goal is to fine-tunethe Voltage-Controlled Chrystal Oscillator (VCXO) an embodiment may beused that simplifies the necessary sampling and computation describedabove to a minimum and just extract the minimal information needed fromeach iteration to figure out which direction the reference frequencyneeds to be corrected. Then a feedback loop such as a PLL can beemployed to settle at the final corrected value. The sweeping range maystart initially large to cover the whole possible error range. However,the range and increment can be reduced to a much finer value after a fewiterations, which in turn minimizes delay in the feedback and allowwider loop bandwidth for better noise shaping of the reference crystaloscillator (VCXO). Another alternative method is to adjust a fractionaldivision ratio of a phase locked loop (PLL) as a way to correct thefrequency error instead of tuning reference crystal oscillator once theprecise amount of frequency error has been extracted using the methoddescribed above.

Thus, in one embodiment depicted in FIG. 11, a method 1100 comprises:generating a baseband information signal at 1102 by mixing a receivedmodulated carrier signal with a local oscillator (LO) signal having anLO frequency; obtaining baseband signal samples of the basebandinformation signal having an in-phase signal sample and a quadraturesignal sample at 1104; determining an offset frequency rotation based onan estimated correlation between the in-phase signal samples and thequadrature signal samples at 1106; and, processing the basebandinformation signal using the offset frequency rotation at 1108. In oneembodiment, the method of processing the baseband information signalusing the offset frequency rotation comprises adjusting the LO frequencyusing an LO control signal. In a further embodiment, the method ofapplying a correction signal comprises applying a complex rotation tothe baseband signal samples. In some embodiments, the estimatedcorrelation between the in-phase signal samples and the quadraturesignal samples is based on squared in-phase samples and squaredquadrature samples. In alternative embodiments, the estimatedcorrelation between the in-phase signal samples and the quadraturesignal samples is based on absolute values of in-phase samples andabsolute values of quadrature samples.

Embodiments described herein may further utilize a phase alignmenttechnique. One implicit assumption made in the previous section is thatat the beginning of the observation window (t=0) there is perfect phasealignment. However, in reality the demodulated complex signal (I+j*Q)contains a phase offset as well as the frequency offset stemming fromthe synchronization error, ε. However, it turns out that the correlationbehavior of the sampled data set described in section 3.3 also provideinformation about the phase offset of the data. When there is zero phaseoffset at time t=0, the tabulated data set, S in equation (3.18)exhibits symmetry about the i'th set that represents the leastcorrelation. This is because the correlation expressions shown in (3.16)and (3.17) are even functions. However, if there is a phase offset att=0, this symmetry is lost as illustrated in FIG. 9 with simulationresults.

FIG. 9 depicts plots of estimated correlations with phase offsets usingdata samples without a frequency offset synchronization error. Asexpected without phase offset (902) the maximum value occurs when nofrequency correction is made (mid-point) and the overall plot maintainssymmetry about y-axis. However, with the remaining curves that aregenerated from sample data having phase offsets (but no frequencyoffsets), the peak value occurs at a wrong frequency offset correctionvalue and the overall plot does not show even-function symmetry.

Some embodiments may utilize this symmetry property and can accomplishboth phase alignment and frequency synchronization at the same time.Thus, in one embodiment, after obtaining the tabulation of frequencyerror correction on the sampled data set, the algorithm may adjust aphase offset of the data set until the symmetry is established. This isa fairly straight forward procedure that may sweep phase values between0 and π/2 until the data set exhibits symmetry. This symmetry repeatsevery π/2 since I and Q are offset by π/2. In order to accomplishcorrect frequency synchronization, the algorithm may perform phasealignment using this procedure first. As the frequency error, ε, getssmaller after each iteration, the required phase alignment gets smalleras well.

As shown in FIG. 10, correlation estimates are plotted with the variousindicated phase offset corrections for an IQ complex sample data setthat contains a −10 frequency offset error and no phase offset. Asexpected, the curve without the phase offset shows the correct frequencyoffset correction.

Phase alignment in a communication channel typically may be done at anupper layer after frequency synchronization has been accomplished.Therefore, accomplishing phase alignment while performing frequencysynchronization is an attractive feature, especially where the systemdeploys multiple receivers as in MIMO radios, or Active Array Antennas.

It is also worth noting here that if ε is too great to start with, theappropriate sample size (time window) required to observe the integratedcosine curve becomes too small. Then a single capture of the data set isstatistically too unreliable in order to observe the integrated cosinepattern, which prevents proper phase correction. For this reason thishigh precision synchronization methods described above are mainlysuitable once a reasonable synchronization is achieved first.Consequently, this method combined with the wide-range synchronizationmethod described above would work well if the expected frequency erroris rather high to begin with.

FIG. 12 depicts one embodiment of a synchronization and phase alignmentprocess. However, it is just one example and there can be many variantsthat utilize the correlation behavior between I and Q in the presence ofsynchronization error as described in this document. The example flowchart describing the carrier synchronization and phase alignmentprocedure may be repeated continuously or periodically at a slowinterval since tracking the frequency errors in reference crystaloscillators doesn't need to be fast. In a further embodiment, a method1200 comprises: generating a baseband information signal by mixing areceived modulated carrier signal with a local oscillator (LO) signalhaving an LO frequency at 1202; obtaining baseband signal samples of thebaseband information signal having an in-phase signal sample and aquadrature signal sample at 1204; determining a phase offset and anoffset frequency rotation based on an estimated correlation between thein-phase signal samples and the quadrature signal samples at 1206; and,processing the baseband information signal using the offset frequencyrotation and offset phase at 1208. In one embodiment, the offset phaseis determined based on a symmetry property of the estimated correlation,the symmetry property being measured with respect to a plurality offrequency offsets. In further embodiments, the offset frequency rotationand the offset phase is determined with respect to a maximum value ofthe estimated correlation, wherein the estimated correlation iscalculated using a plurality of candidate phase offsets over a firstrange and candidate frequency rotations over a second range. In furtherembodiments, the method may include updating the offset frequencyrotation and the offset phase by recalculating a maximum value of theestimated correlation, wherein at lease one of the first range and thesecond range is reduced. Further, the estimated correlation between thein-phase signal samples and the quadrature signal samples is based onsquared in-phase samples and squared quadrature samples or on absolutevalues of in-phase and quadrature signals.

The frequency synchronization embodiments described herein may becombined with positioning systems and methods. With respect to FIG. 13,the calculations for the Range (Distance) between two nodes (Cell A andCell B), once the two nodes are frequency synchronized, will bedescribed. T₁: time information at cell A is sent to cell B (T_(1A));T₂: T_(1A) received time at B (T_(2B)); T₃: time information sent fromcell B to cell A (T_(2B), T_(3B)); T₄: T_(2B), T_(3B) received time atcell A (T_(4A)); T_(1A) and T_(4A) are local time at cell A, T_(2B) andT_(3B) are local time at cell B; D_(R) is the actual time delay due tothe distance (range delay); D_(B) is the processing time delay at cellB; {tilde over (D)}{tilde over (D_(R))} is the range delay estimatecalculated at cell A, and ε_(fA), ε_(fB) are the reference frequencyerrors at cell A and cell B.

Here T₁, T_(z), T₃, T₄ denote ideal time. The above example describedwith respect to FIG. 13 illustrates how Cell A may figure out thedistance between Cell A and Cell B from a single exchange of timeinformation. First, Cell A sends its local time information (T_(1A)) attime T₁. Then, Cell B records the arrival time (T₂) according to itslocal time, which is denoted as T_(2B). Cell B then transmits back toCell A this arrival time at time T₃ along with the transmit time(T_(3B)). These two values (T_(2B) and T_(3B)) are all that Cell A needsin order to figure out what the actual time delay is due to the actualdistance between the two Cells because it has a synchronized referencefrequency. The derivation of the delay expression is shown below.

$\begin{matrix}\begin{matrix}{\overset{\sim}{D_{R}} = {0.5 \cdot \left\{ {\left( {T_{4,A} - T_{1\; A}} \right) - \left( {T_{3\; B} - T_{2\; B}} \right)} \right\}}} \\{= {0.5 \cdot \left\{ {{\left( {T_{4} - T_{1}} \right)\left( {1 + ɛ_{f\; A}} \right)} - {\left( {T_{3} - T_{2}} \right)\left( {1 + ɛ_{f\; B}} \right)}} \right\}}} \\{= {{D_{R}\left( {1 + ɛ_{f\; A}} \right)} + {0.5 \cdot {D_{B}\left( {ɛ_{f\; A} - ɛ_{f\; B}} \right)}}}} \\{\cong {D_{R} + {0.5 \cdot {D_{B}\left( {ɛ_{f\; A} - ɛ_{f\; B}} \right)}}}}\end{matrix} & (3.23)\end{matrix}$

Here, ε_(fA) and ε_(fB) represent reference frequency error at nodescell A and cell B respectively expressed in fraction, e.g., if cell Ahas 1 ppm of frequency error, ε_(fA) would be 1e-6. The D_(R) value iscalculated, which represents the time delay of a radio wave travellingat the speed of light from Cell A to Cell B. This value will be ingeneral quite small; for example, 300 meter distance will cause 1 ustime delay. On the other hand, D_(B) represents the processing delay atCell B which can easily be several milliseconds. Therefore, as can beseen in equation (3.23), when the two nodes are not synchronized infrequency, the resulting calculation can easily be dominated by thisprocess delay time, D_(B), which makes this method ineffective. However,once the two nodes are frequency-synchronized, the second term in (3.23)drops out and the calculation accurately shows the actual time delaybetween the two nodes.

As can be seen in this calculation the delay estimate error is afunction of the relative frequency error (synchronization error) and theabsolute frequency error has negligible impact on the accuracy of therange delay calcuation as long as the frequency error at both cells arethe same—in other words, as long as ε_(fA)=ε_(fB). It also shows thatthe absolute time error cancels out in the delay expression, and it hasa negligible impact on the accuracy of the range delay, D_(R)measurement.

Thus in still further embodiments, the synchronization techniques may beused in conjunction with a time synchronization protocol as describedwith respect to FIG. 13.

Further embodiments include the use of the above-described frequencysynchronization techniques for network time synchronization. Oncenetwork nodes are able to achieve a high-degree of frequencysynchronization using the methods and devices described above, networktime synchronization may be achieved by various nodes in the network.Since all nodes are synchronized in their reference frequency and theirrelative distances can also be determined according to the methoddescribed above, each node may engage in an exchange of time informationfrom a reference node (a master node), which provides the master clockfor the network. Since the time delay from the master node can preciselybe measured using the positioning method described above (or in manyapplications, it might be already known by other means), each node cancalculate the precise time synchronized to the master clock from asingle exchange of time information. Frequency and time synchronizationsteps could repeat at a set interval to maintain a high degree ofsynchronization against temporal perturbations in the network.

Positioning systems employing the frequency synchronization techniquemay include mobile-to-mobile positioning systems, mesh network systems,and network systems.

In a mobile-to-mobile positioning systems, the frequency synchronizationand positioning algorithms and methodology described above may beemployed in a group of radios that are designed to communicate to oneanother. Given any two radios in communication, the methods describedabove allow both parties to calculate the distance between the two. Thisis illustrated in FIG. 14.

In addition, because the frequency synchronization algorithm alsoextracts the phase offset of the arrived signal as a bi-product, the useof a multi-input receiver also allows the system to calculate thearrival angle of the incoming radio wave simply by comparing this phaseoffset adjustment at the two inputs of the receiver. Having the angle ofarrival along with the distance information allows one radio unit tolocate the target location in two-dimensional space such as flat surfaceareas. If the Receiver is equipped with three receivers, the target canbe located in three-dimensional space.

The mobile-to-mobile positioning system is illustrated in FIG. 15,where:

${\cos\;\theta} = {{\frac{l}{d}\mspace{14mu}{and}\mspace{14mu}\frac{l}{\lambda}} = \frac{\delta\;\phi}{2}}$And therefore,

$\theta = {\cos^{- 1}\left( \frac{c\;\delta\;\phi}{2\;\pi\; d\; f} \right)}$where δφ is the phase offset difference between two received signals atnode A, and λ and f are the wavelength and frequency, and c is the speedof light.

In a mesh network positioning system, a mesh network with multipleindividual mobile radios can collectively use the frequencysynchronization and positioning algorithms described above in order tofigure out relative positions of each of the mesh nodes. Described belowis the case with four mobile units where each unit can figure out thedistance to the other three units using the method described above. Bysharing the distance information from one another the nodes can figureout that the relative location of all four with respect to one anothercan only have two possible solutions as illustrated in the diagram FIG.16. Therefore, only with one extra piece of information addressing theacceptable orientation of the two solutions, the nodes can determineexactly where each node is with respect to one another using thepositioning method described above. Furthermore, figuring out initialorientation only requires three units to initialize their relativelocation and orientation. This can easily be accomplished asinitialization process for the mesh network prior to deploying thenetwork.

In a network-based positioning system, frequency and timesynchronization of all the network nodes is achieved using the methodsdescribed above. Once this is accomplished, the network can determinewhere the individual mobile units are located. Two methods of locatingindividual users (User Equipments: UE) in a network are described below.

In a network-based positioning system, uplink signals, as shown in FIG.17, may be used. Because the network nodes are already “perfectly”synchronized, the network may compare the arrival times of the mobileunit signal from mobile unit 1708 from several network nodes 1702, 1704,and 1708 (e.g., base stations). In the embodiment of FIG. 17, thearrival times at network nodes 1702 and 1704 are conveyed to node 1708for comparison. The difference in arrival time indicates the differencein distance between the mobile unit and the network nodes. Ideally onlythree measurements involving three network nodes may be used to locatethe mobile unit in a three dimensional space. In this method,positioning of mobile unit does not require the mobile unit radio (UE)to be synchronized with network nodes. This method also requires nocalculation to be performed in mobile unit.

In network-based positioning systems, downlink signals may be used asshown in FIG. 18. In this embodiment of the network-based positioningsystem utilizing the frequency synchronization and positioningalgorithms described herein, network nodes 1802, 1804, and 1808broadcast timing and positioning information. Individual mobile unitssuch as unit 1806 may receive these signals from the multiple networknodes (base stations) and calculate its own position using atrilateration method.

From the foregoing, it will be clear that the present invention has beenshown and described with reference to certain embodiments that merelyexemplify the broader invention revealed herein. Certainly, thoseskilled in the art can conceive of alternative embodiments. Forinstance, those with the major features of the invention in mind couldcraft embodiments that incorporate one or major features while notincorporating all aspects of the foregoing exemplary embodiments.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 10%, inanother embodiment within 5%, in another embodiment within 1% and inanother embodiment within 0.5%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (e.g., comprising a processor) to perform amethod as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory) and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

I claim:
 1. A blind frequency synchronization method comprising:generating a baseband information signal by mixing a received modulatedcarrier signal with a local oscillator (LO) signal having an LOfrequency; obtaining baseband signal samples of the baseband informationsignal having a baseband signal magnitude and a baseband signal phase;determining a cumulative phase measurement associated with basebandsignal samples having a baseband signal magnitude greater than athreshold; and, applying a correction signal to compensate for an LOfrequency offset of the LO frequency based on the cumulative phase,wherein the cumulative phase measurement is based on an instantaneousphase of a data signal within the received modulated carrier signal, andwherein determining a cumulative phase measurement comprisesaccumulating a plurality of differential phases between successivecomplex signal points, each of the successive complex signal pointshaving magnitude greater than the threshold.
 2. The method of claim 1wherein the LO signal includes an in-phase carrier signal and aquadrature carrier signal, and the baseband signal samples aredetermined from an in-phase channel sample and a quadrature channelsample.
 3. The method of claim 1 wherein applying a correction signalcomprises adjusting an LO control signal.
 4. The method of claim 1wherein applying a correction signal comprises applying a complexrotation to the baseband signal samples.
 5. The method of claim 1,further comprising calculating an instantaneous derivative of a firstbaseband signal sample and of a second baseband signal sample, the firstand second baseband signal samples each having a baseband signalmagnitude greater than the threshold.
 6. The method of claim 5, furthercomprising accumulating the instantaneous derivatives of the firstbaseband signal sample and the second baseband signal sample, therebyaccumulating cumulative phase measurements.
 7. An apparatus comprising:a mixer and a local oscillator (LO) configured to generate a basebandinformation signal by mixing a received modulated carrier signal with anLO signal having an LO frequency; an analog to digital converterconfigured to generate baseband signal samples of the basebandinformation signal; a phase accumulator configured to receive thebaseband signal samples and to determine a cumulative phase measurementassociated with baseband signal samples having a baseband signalmagnitude greater than a threshold; and, an LO correction moduleconfigured to apply a correction signal to compensate for an LOfrequency offset of the LO frequency based on the cumulative phase,wherein the cumulative phase measurement is based on an instantaneousphase of a data signal within the received modulated carrier signal, andwherein the phase accumulator is further configured to accumulate aplurality of differential phases between successive complex signalpoints, each of the successive complex signal points having magnitude.8. The apparatus of claim 7 wherein the LO includes an in-phase carriersignal generator and a quadrature carrier signal generator.
 9. Theapparatus of claim 7 wherein the cumulative phase measurement isdetermined over either (i) a predetermined time interval or (ii) apredetermined number of samples.
 10. The apparatus of claim 7 whereinthe LO correction module comprises an LO control module configured togenerate a control signal.
 11. A method comprising: generating abaseband information signal by mixing a received modulated carriersignal with a local oscillator (LO) signal having an LO frequency, themodulated carrier signal being an in-phase signal and quadrature signaluncorrelated with each other and derived from different input data sets;obtaining baseband signal samples of the baseband information signalhaving an in-phase signal sample and a quadrature signal sample;determining an offset frequency rotation based on an estimatedcorrelation between the in-phase signal samples and the quadraturesignal samples; and, processing the baseband information signal usingthe offset frequency rotation, wherein the received modulated carriersignal is a quadrature-modulated signal with arbitrary orthogonalin-phase and quadrature signal components.
 12. The method of claim 11wherein processing the baseband information signal using the offsetfrequency rotation comprises adjusting the LO frequency using an LOcontrol signal.
 13. The method of claim 11 further comprising:determining an offset phase associated with the estimated correlationbetween the inphase signal samples and the quadrature signal samples;and, processing the baseband information signal using the offset phase.14. The method of claim 11 wherein the estimated correlation between thein-phase signal samples and the quadrature signal samples is based onsquared in-phase samples and squared quadrature samples.
 15. The methodof claim 11 wherein the estimated correlation between the in-phasesignal samples and the quadrature signal samples is based on absolutevalues of in-phase samples and absolute values of quadrature samples.16. The method of claim 11, wherein determining the offset frequencyrotation further comprises time averaging and integrating a product ofeither squares of or absolute values of the in-phase signal and thequadrature signal.
 17. The method of claim 11, wherein determining theoffset frequency rotation further comprises calculating:Σ_(k=1) ^(N) {|I _(R,i)(k)|·|Q _(R,i)(k)|}, where I_(R,i) refers to anin-phase part of an ith error-corrected data set and Q_(R,i) refers to aquadrature part of the ith error-corrected data set.
 18. The method ofclaim 11, wherein determining the offset frequency rotation furthercomprises calculating:Σ_(k=1) ^(N){(I _(R,i)(k))²(Q _(R,i)(k))²}, where I_(R,i) refers to anin-phase part of an ith error-corrected data set and Q_(R,i) refers to aquadrature part of the ith error-corrected data set.